Feature Articles

Microarrays for Gene-Expression Studies

Cost-Effective Alternative to Sequencing Boasts a Multitude of Applications

Reverse Engineering

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MYB/FOXM1 network from the Human B Cell Interactome (HBCI)

“The work that we do with microarrays is quite different from much of the field,” says Andrea Califano, Ph.D., professor in the department of biomedical informatics at Columbia University Medical Center. “We use them to reverse engineer the regulatory network controlling cell behavior and then to identify master regulators of specific phenotypes. Instead of looking for genes that are differentially expressed, we associate regulatory changes in the cell with genes that control them. This typically requires analyzing hundreds of samples at a time.”

The end result is that Dr. Califano’s lab has assembled genome-wide, context-specific maps of molecular interactions in human cells by integrating several reverse-engineering approaches, which include the use of microarrays. “We have a good repertoire of control mechanisms of the cell, including transcriptional, post-transcriptional, and post-translational ones,” notes Dr. Califano, “We use microarrays to define cellular transition, and to have a complete map of regulatory changes in the cell. These resulting maps have shown significant promise in the rational elucidation of both physiological and pathological phenotypes.”

Their approach, using microarray data to reverse engineer the regulatory process of the cell, makes it possible to infer robust prognostic markers by identifying upstream master regulators that are causally related to the presentation of the phenotype of interest. “You couldn’t do this with sequencing at this stage because profiling hundreds of samples by deep sequencing is still prohibitively expensive,” Dr. Califano adds. “We have, however, successfully used deep sequencing for short regulatory RNA profiling.”

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